Optimization of Collocated/Noncollocated Sensors and Actuators along with Feedback Gain Using Hybrid Multiobjective Genetic Algorithm-Artificial Neural Network. (19th February 2014)

• Record Type: Journal Article
• Authors: Chhabra, Deepak; Bhushan, Gian; Chandna, Pankaj
• Other Names: Guan X. Academic Editor.; Sun B. Academic Editor.; Xie G. Academic Editor.
• Is Part Of: Chinese journal of engineering. Volume 2014(2014)
• Publication Date: 2014-02-19
• Subjects: Engineering -- Periodicals; Engineering; Periodicals
• DOI: 10.1155/2014/692140
• Languages: English
• ISSNs: 2314-8063

Abstract

Abstract : A multiobjective optimization procedure is proposed to deal with the optimal number and locations of collocated/noncollocated sensors and actuators and determination of LQR controller gain simultaneously using hybrid multiobjective genetic algorithm-artificial neural network (GA-ANN). Multiobjective optimization problem has been formulated using trade-off objective functions ensuring good observability/controllability of the structure while minimizing the spillover effect and maximizing closed loop average damping ratio. Artificial neural networks (ANNs) are used to train the input as varying numbers and placements of sensors and actuators and the outputs are taken as the three objective functions (i.e., controllability, observability, and closed loop average damping ratio), thus forming three ANN models. The trained mathematical models of ANN are fed into the multiobjective GA. The hybrid multiobjective GA-ANN maintains the trade-off among the three objective functions. The ANN3 model is used experimentally to provide the control inputs to the piezoactuators. It is shown that the proposed method is effective in ascertaining the optimal number and placement of actuators and sensors with consideration of controllability, observability, and spillover prevention such that the performance on dynamic responses is also satisfied. It is also observed that damping ratio obtained with hybrid multiobjective GA-ANN and found with ANN experimentally/online holds well in agreement.